Senior ML Platform Engineer

About the Team & Mission

At 42dot, our AD ML Platform Engineers build the core data platform and ML training / eval platform for the cutting edge algorithms in autonomous driving. We develop the distributed system of a scalable data platform for large-scale dataset (millions of scenes), as well as high-performance data serving SDKs for ML model training / evaluation.

The platforms we deliver could highly improve the efficiency of ML model development lifecycle, including training, evaluation, deployment, as well as monitoring in the cloud environment.

Responsibilities

• Set technical strategy and oversee development of high scale, reliable data platform to manage, visualize and serve large-scale datasets for ML model training and validation.
• Build up the data lakehouse for autonomous driving scene datasets, including the sensor data, calibration data, as well as annotation data
• Drive the Autonomous Driving Data SDK development, including scene data search, datasets preparation, dataset loading, etc.
• Dig into performance bottlenecks all along the data processing pipelines, from data processing latency, data search latency to Test Procedure (TP) coverage.
• Bootstrap and maintain infrastructure for Data Platform components—Data Processing Pipeline, Database, Data Lakehouse and Data Serving.
• Collaborate with cross-functional teams, including ML algorithm, ML application, and Cloud Infra to align ML Platforms with overall Autonomous Driving System Architecture.

Qualifications

• Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.
• Minimum of 7 years of experience in Data Engineering or ML Platform roles
• Expert-level proficiency in Python and solid experience in Python SDK development
• Solid working experience in Databases (e.g., MongoDB, PostgreSQL, etc)
• Strong understanding of modern AI frameworks (e.g., PyTorch, TensorFlow etc.), especially the principle of distributed data loader for model training
• Hands-on experience with data pipeline job orchestration with Databricks Workflows or Apache Airflow, as well as integrating data pipelines with machine learning models
• Extensive experience with data technologies and architectures such as Data Warehouse (e.g., Hive) or Lakehouse (e.g., Delta Lake)
• Experience with Apache Spark or other big data computing engines
• Excellent leadership and communication skills, with a demonstrated ability to lead technical projects

Preferred Qualifications

• Experience with autonomous vehicle sensor data (e.g., LiDAR, camera, radar)
• Experience with ML model training lifecycle (e.g., data preparation, model training / validation / deployment, etc)
• Understanding data governance principles, data privacy regulations, and experience implementing security measures to protect data
• Understanding of Large Models, like VLM
• Interview Process
• Application Screening
• Coding Test
• First Interview (Virtual, approximately 1 hour)
• Second Interview (In-person or Virtual, approximately 3 hours)
• Offer Discussion / Onboarding
• Additional Information
• The recruitment process may change depending on schedule and progress; the result of each stage will be sent individually to your registered email.
• Please do not include legally prohibited information in your application (e.g., ID number, family relations, marital status, salary, photo, physical details, hometown).
• For application errors or inquiries, contact recruit@42dot.ai.
• Veterans and applicants eligible for employment protection will receive preferential consideration in accordance with applicable laws and regulations.
• In compliance with the Act on Employment Promotion and Vocational Rehabilitation for Persons with Disabilities, registered individuals with disabilities will receive preferential consideration.
• 42dot does not accept unsolicited resumes from search firms. We will not pay any fees for resumes submitted without prior agreement.
• False information in your application may result in offer cancellation.
• A reference check may be conducted after the interview process, with your consent.
• A 3-month probationary period may apply.